随笔分类 - Recommender Systems
摘要：目录概统计角度论证实验论证代码 Woolridge D., Wilner S. and Glick M. An analysis of sequential recommendation datasets. PERSPECTIVES, 2021. Harper F. M. and Konstan J
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摘要：目录概符号/缩写说明Training detailsDatasetsE2E 下 MoRec 是否优于 IDRec?Regular settingWarm setting越好的 encoder 带来越好的推荐效果?TS versus E2E?总结代码 Yuan Z., Yuan F., Song Y.
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摘要：目录概符号说明Softmax lossBilateral Softmax loss (BSL)代码 Wu J., Chen J., Wu J., Shi W., Zhang J. and Wang X. BSL: Understanding and Improving Softmax Loss fo
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摘要：目录概符号说明DualGCN代码 Wang Q., Wei Y., Yin J., Wu J., Song X. and Nie L. DualGNN: Dual graph neural network for multimedia recommendation. IEEE Transaction
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摘要：目录概符号说明MGCNMotivationBehavior-Guided PurifierMulti-View Information EncoderBehavior-Aware FuserPredicitonOptimation代码 Yu P., Tan Z., Lu G. and Bao B.
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摘要：目录概FREEDOMMotivationFrozen Item-Item graphDenoising User-Item Bipartite GraphTwo Graphs for Learning代码 Zhou X. and Shen Z. A tale of two graphs: Freez
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摘要：目录概符号说明LATTICEModality-aware Latent Structure LearningCombining with Collaborative Filtering代码 Zhang J., Zhu Y., Liu Q., Wu S., Wang S. and wang L. Mi
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摘要：目录概符号说明KGATEmbedding LayerAttentive Embedding Propagation Layers代码 Wang X., He X., Cao Y., Liu M. and Chua T. KGAT: Knowledge graph attention network
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摘要：目录概符号说明MotivationLTGNN代码 Zhang J., Xue R., Fan W., Xu X., Li Q., Pei J. and Liu X. Linear-time graph neural networks for scalable recommendations. WWW
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摘要：目录概符号说明MMGCN代码 Wei Y., Wang X., Nie L., He X., Hong R. and Chua T. MMGCN: Multi-modal graph convolution network for personalized recommendation of mic
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摘要：目录概InstructRecInstruction Generation Zhang J., Xie R., Hou Y., Zhao W. X., Lin L., Wen J. Recommendation as instruction following: a large language mo
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摘要：[TOC] Guo J., Du L, Chen X., Ma X., Fu Q., Han S., Zhang D. and Zhang Y. On manipulating signals of user-item graph: A jacobi polynomial-based graph c
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摘要：目录概TallRec代码 Bao K., Zhang J., Zhang Y., Wang W., Feng F. and He X. TALLRec: An effective and efficient tuning framework to align large language model
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摘要：目录概符号说明Pixie Eksombatchai C., Jindal P., Liu J. Z., Liu Y., Sharma R., Sugnet C., Ulrich M. and Leskovec J. Pixie: A system for recommending 3+ billio
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摘要：目录概TGINMotivation: Triangle 的重要性Model代码 Jiang W., Jiao Y., Wang Q., Liang C., Guo L., Zhang Y., Sun Z., Xiong Y. and Zhu Y. Triangle graph interest ne
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摘要：目录概DG-ENN Guo W., Su R., Tan R., Guo H., Zhang Y., Liu Z., Tang R. and He X. Dual graph enhanced embedding neural network for ctr prediction. KDD, 202
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摘要：目录概Fi-GNN代码 Li Z., Cui Z., Wu S., Zhang X. and Wang L. Fi-GNN: Modeling feature interactions via graph neural networks for ctr prediction. CIKM, 2019.
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摘要：目录概符号说明SimRecPrediction-Level DistillationEmbedding-level DistillationAdaptive Contrastive Regularization总的损失代码 Xia L., Huang C., Shi J. and Xu Y. Gra
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摘要：目录概UnKD代码 Chen G., Chen J., Feng F., Zhou S. and He X. Unbiased knowledge distillation for recommendation. WSDM, 2023. 概 考虑流行度偏差的知识蒸馏, 应用于推荐系统. UnKD M
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摘要：目录概DE-RRDDistillation Experts (DE)Relaxed Ranking Distillation (RRD)代码 Kang S., Hwang J., Kweon W. and Yu H. DE-RRD: A knowledge distillation framewor
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